
Ablation studies on our loss functions was implemented to test model robustness in general for the CIFAR-10, FairFace and CelebA datasets, respectively. The Lagrange coefficients \(\lambda _{1} \)  and \( \lambda _{2} \)  of the SSIM and NIQE losses were also changed empirically within (\( 0.001 \ge \lambda _{1} \)  and \( \lambda _{21}\le 1.000\) ) range, to check the effect on the perceptual appeal of the synthesised images. It was inferred that reducing the coefficients towards the lower limit weakens the discriminative power which in turn reduces the quality of the synthesised images from the generator.
We also conducted a Histogram of Oriented Gradient (HOG) similarity performance with the Inception v3 model [1]} for the input and synthesised images on the FairFace dataset, in order to obtain the model layer-wise performance at specific iterations of the baseline of our model. Figure 6 (a) shows the HOG similarity performance at different iterations while training for our model compared to other quality metric techniques.
